Abstract #0800
Combining HARDI Datasets With More Than One bValue Improves Diffusion MRI-Based Cortical Parcellation
Zoltan Nagy 1,2 , Tara Ganepola 3 , Martin I Sereno 3 , Nikolaus Weiskopf 1 , and Daniel C Alexander 4
1
Wellcome Trust Centre for Neuroimaging,
University College London, London, United Kingdom,
2
Laboratory
for Social and Neural Systems Research, University of
Zrich, Zrich, Switzerland,
3
Department
of Cognitive, Perceptual and Brain Sciences, University
College London, United Kingdom,
4
Center
for Medical Image Computing, University College London,
United Kingdom
MRI based invivo histology of brain tissue is an active
research area with several approaches using different
contrasts. Previously, we have used high angular
resolution diffusion imaging data with a single bvalue
to construct a feature vector, which we proposed as a
method for grey matter cortical parcellation. Here, we
investigate the utility of combining data from several
bvalues (i.e. constructing a 2D feature matrix). The
results strongly suggest that the combining information
contained in these different datasets improves the
parcellation. Future work will refine the choice of
bvalues and focus on histilogical validation.
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